The graph represents a network of 4,337 Twitter users whose recent tweets contained "meded", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 2/12/2023 5:00:35 PM. The network was obtained from Twitter on Monday, 13 February 2023 at 06:37 UTC.
The tweets in the network were tweeted over the 2013-day, 20-hour, 39-minute period from Wednesday, 09 August 2017 at 04:20 UTC to Monday, 13 February 2023 at 01:00 UTC.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above.
The graph is directed.
The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Author Description
Vertices : 4337
Unique Edges : 2867
Edges With Duplicates : 8704
Total Edges : 11571
Number of Edge Types : 9
Retweet : 4227
MentionsInRetweet : 5033
Tweet : 793
Mentions : 724
Replies to : 158
MentionsInReplyTo : 403
Quote : 81
MentionsInQuote : 141
MentionsInQuoteReply : 11
Self-Loops : 1009
Reciprocated Vertex Pair Ratio : 0.0229007633587786
Reciprocated Edge Ratio : 0.0447761194029851
Connected Components : 365
Single-Vertex Connected Components : 113
Maximum Vertices in a Connected Component : 2847
Maximum Edges in a Connected Component : 8419
Maximum Geodesic Distance (Diameter) : 14
Average Geodesic Distance : 4.293007
Graph Density : 0.000313529766609633
Modularity : 0.453476
NodeXL Version : 1.0.1.510
Data Import : The graph represents a network of 4,337 Twitter users whose recent tweets contained "meded", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 2/12/2023 5:00:35 PM. The network was obtained from Twitter on Monday, 13 February 2023 at 06:37 UTC.
The tweets in the network were tweeted over the 2013-day, 20-hour, 39-minute period from Wednesday, 09 August 2017 at 04:20 UTC to Monday, 13 February 2023 at 01:00 UTC.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above.
Layout Algorithm : The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Graph Source : TwitterSearch2
Graph Term : meded
Groups : The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
Edge Color : Edge Weight
Edge Width : Edge Weight
Edge Alpha : Edge Weight
Vertex Radius : Betweenness Centrality
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[1187] #medtwitter,#meded [1104] #meded,#medtwitter [406] ekg,changes [406] changes,hyperkalemia [404] hyperkalemia,#medtwitter [322] #medtwitter,#foamed [276] #medtwitter,#medicine [266] #tipsfornewdocs,#meded [258] #medicine,#medical [252] #medical,#medicare Top Word Pairs in Tweet in G1:
[566] #medtwitter,#meded [400] #meded,#medtwitter [374] ekg,changes [374] changes,hyperkalemia [373] hyperkalemia,#medtwitter [203] brownjhm,ekg [189] viral,rashes [189] pedscases,#meded [189] pediatric,viral [189] rashes,pedscases Top Word Pairs in Tweet in G2:
[472] #meded,#medtwitter [325] #medtwitter,#meded [232] #tipsfornewdocs,#meded [225] #medtwitter,#medicine [224] #medicine,#medical [219] #medical,#medicare [215] #medicare,#health [214] #health,#healthcare [200] #healthcare,#foamed [113] imedverse,diagnosis Top Word Pairs in Tweet in G3:
[87] #meded,#impocus [83] #anatomy,co [83] dvt,#pocus [83] thqzkuwa2k,#meded [83] co,thqzkuwa2k [83] #pocus,#anatomy [82] nephrop,dvt [56] #meded,#foamcc [54] circulatory,support [53] 9p5wrkwbzu,#meded Top Word Pairs in Tweet in G4:
[56] abdominal,pain [34] leg,raising [34] carnett,sign [34] head,leg [34] along,rectus [34] rectus,sheath [34] sign,refers [34] focal,abdominal [34] sheath,worse [34] pain,along Top Word Pairs in Tweet in G5:
[24] #meded,#medtwitter [14] #meded,#foamed [13] #medtwitter,#meded [10] meded,rabbi [10] meded,yâ [10] selamet,meded [10] selim,selamet [9] hem,börtü [9] #epidtwitter,#meded [9] #meded,#epidemiology Top Word Pairs in Tweet in G6:
[90] #medtwitter,#meded [66] #medpub,#medtwitter [56] #meded,#mednews [15] #meded,#medtwitter [14] #cardiotwitter,#meded [8] neuroscience,news [8] update,online [8] disease,#medpub [8] medical,update [8] news,#medpub Top Word Pairs in Tweet in G7:
[83] #foamed,#meded [83] #medtwitter,#foamed [44] risk,factors [41] factors,etiology [41] atherosclerosis,#medtwitter [41] etiology,atherosclerosis [41] #meded,#cardioed [40] manualomedicine,risk [34] #meded,#usmle [27] manualomedicine,acute Top Word Pairs in Tweet in G8:
[34] #medicine,#meded [34] answer,#ortho [25] #meded,#medstudents [24] #medstudents,#msk [24] #orthopedics,#medicine [24] normal,answer [24] #ortho,#orthopedics [24] #msk,#radiology [24] fracture,normal [23] radrounds,fracture Top Word Pairs in Tweet in G9:
[64] node,anatomy [64] remember,cervical [64] lymph,node [64] questions,remember [64] 2b,question [64] cervical,lymph [64] question,questions [63] teachplaygrub,2b [63] anatomy,lev [5] 23,registration Top Word Pairs in Tweet in G10:
[18] eye,#meded [18] anatomy,eye [17] innov_medicine,anatomy [15] #meded,#medtwitter [10] frronconi,nicochan33 [10] healthcareldr,frronconi [9] make,heart [9] aging,gene [9] anti,aging [9] heart,10 Top Replied-To in Entire Graph:
Top Replied-To in G4:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
Top Mentioned in G2:
Top Mentioned in G3:
Top Mentioned in G4:
Top Mentioned in G5:
Top Mentioned in G6:
Top Mentioned in G7:
Top Mentioned in G8:
Top Mentioned in G9:
Top Mentioned in G10:
Top Tweeters in Entire Graph:
Top Tweeters in G1:
Top Tweeters in G2:
Top Tweeters in G3:
Top Tweeters in G4:
Top Tweeters in G5:
Top Tweeters in G6:
Top Tweeters in G7:
Top Tweeters in G8:
Top Tweeters in G9:
Top Tweeters in G10: